Before performing any mathematical or string operations, we need to determine the column type. String columns cannot have values added or subtracted. However, if we have a column type, we can conditionally apply it.

The easiest way to get the column type of Pandas DataFrame is by using the “.dtypes” attribute. The “dtypes” attribute returns a Series with the data type of each column.

Get the data type of all columns

import pandas as pd

data = {'Name': ["E", "M", "S"],
        'Price': [85, 121, 128],
        'City': ['New York', 'Sydney', 'Perth']}

df = pd.DataFrame(data)

print(df.dtypes)

Output

You can see from the above figure that the Name and City columns are of type string, and in Pandas, it is denoted by “object”. The price is a numeric column, so it has a type “int64”.

Get the data type of a specific column

If you want to get the data type of a specific column of a DataFrame, use this syntax: df[‘column_name’].dtype.

import pandas as pd

data = {'Name': ["E", "M", "S"],
        'Price': [85, 121, 128],
        'City': ['New York', 'Sydney', 'Perth']}

df = pd.DataFrame(data)

print(df['Price'].dtype) # int64

Output

Getting the type of mixed values

If a column of DataFrame contains mixed values (mixed data type values), the “.dtype” attribute will return “object” as an output.

import pandas as pd

data = {'Name': ["E", "M", "S", "F"],
        'Price': [85, 121, "128", True],
        'City': ['New York', 'Sydney', 'Perth', "LA"]}

df = pd.DataFrame(data)

print(df['Price'].dtype) # object

Output

Common data types in Pandas

Data Types	Description
object	It is a text or mixed numeric and non-numeric values.
int64	It is a type of integer number.
float64	It is a type of floating-point number.
bool	It is a type for boolean (True or False) values.
datetime64	It is a type for date and time values.
category	It is a data type for categorical values.

Alternate approach

There is an alternate approach to get the data type of a column called the “DataFrame.info()” method. This method basically returns a concise summary of the DataFrame‘s structure.

import pandas as pd

data = {'Name': ["E", "M", "S", "F"],
        'Price': [85, 121, "128", True],
        'City': ['New York', 'Sydney', 'Perth', "LA"]}

df = pd.DataFrame(data)

print(df.info())

Output

In the above screenshot, you can see that it returns the “Dtype” column, which contains the type of each column. It offers a quick overview of the DataFrame’s structure and shows data types for all columns at once.

Apart from column types, it also returns “index”, “column name”, “non-null”, and “count”. Also, returns memory usage.